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A Coevolutionary Approach to Learn Animal Behavior Through Controlled Interaction

By Wei Li, Melvin Gauci and Roderich Groß


This paper proposes a method that allows a machine to infer the behavior of an animal in a fully automatic way. In principle, the machine does not need any prior information about the behavior. It is able to modify the environmental conditions and observe the animal; therefore it can learn about the animal through controlled interaction. Using a competitive coevolutionary approach, the machine concurrently evolves animats, that is, models to approximate the animal, as well as classifiers to discriminate between animal and animat. We present a proof-of-concept study conducted in computer simulation that shows the feasibility of the approach. Moreover, we show that the machine learns significantly better through interaction with the animal than through passive observation. We discuss the merits and limitations of the approach and outline potential future directions

Topics: Categories and Subject Descriptors I.2.6 [Artificial Intelligence, Learning—Knowledge acquisition, I.2.8 [Artificial Intelligence, Problem Solving, Control Methods, and Search—Heuristic methods, I.2.9 [Artificial Intelligence, Robotics—Autonomous vehicles Keywords Science automation, animal behavior, coevolution, Turing test
Year: 2013
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